Overview

Dataset statistics

Number of variables23
Number of observations5282
Missing cells9871
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory913.1 KiB
Average record size in memory177.0 B

Variable types

Numeric6
Text6
Categorical5
Boolean6

Alerts

Modified_Operating_Hours_ has constant value ""Constant
Fuel_Station_open_to_public_ has constant value ""Constant
businessunit_number is highly overall correlated with businessunit_status_code and 2 other fieldsHigh correlation
businessunit_status_code is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
bu_num is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
businessunit_banner_description is highly overall correlated with businessunit_type_descriptionHigh correlation
businessunit_type_description is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
businessunit_isstoreopen is highly overall correlated with businessunit_status_codeHigh correlation
Pharmacy_open_to_public_ is highly overall correlated with Grocery_delivery_status and 1 other fieldsHigh correlation
Grocery_delivery_status is highly overall correlated with Pharmacy_open_to_public_ and 1 other fieldsHigh correlation
Online_grocery_pickup_status is highly overall correlated with Pharmacy_open_to_public_ and 1 other fieldsHigh correlation
businessunit_banner_description is highly imbalanced (56.5%)Imbalance
businessunit_isstoreopen is highly imbalanced (96.3%)Imbalance
op_status is highly imbalanced (99.3%)Imbalance
Pharmacy_open_to_public_ is highly imbalanced (99.7%)Imbalance
Modified_Operating_Hours_ has 5279 (99.9%) missing valuesMissing
Grocery_delivery_service has 467 (8.8%) missing valuesMissing
Grocery_delivery_status has 2417 (45.8%) missing valuesMissing
Online_grocery_pickup has 138 (2.6%) missing valuesMissing
Online_grocery_pickup_status has 1505 (28.5%) missing valuesMissing
objectid has unique valuesUnique
businessunit_number has unique valuesUnique
bu_num has unique valuesUnique

Reproduction

Analysis started2023-10-22 10:06:25.598449
Analysis finished2023-10-22 10:06:34.795134
Duration9.2 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

X
Real number (ℝ)

Distinct5277
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.459635
Minimum-159.36503
Maximum0
Zeros3
Zeros (%)0.1%
Negative5279
Negative (%)99.9%
Memory size41.4 KiB
2023-10-22T12:06:34.924969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-159.36503
5-th percentile-119.17875
Q1-97.117344
median-88.406115
Q3-81.745786
95-th percentile-74.65466
Maximum0
Range159.36503
Interquartile range (IQR)15.371558

Descriptive statistics

Standard deviation13.682249
Coefficient of variation (CV)-0.14959877
Kurtosis2.2599628
Mean-91.459635
Median Absolute Deviation (MAD)7.536797
Skewness-0.86057402
Sum-483089.79
Variance187.20393
MonotonicityNot monotonic
2023-10-22T12:06:35.159782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
0.1%
-84.848736 2
 
< 0.1%
-90.324403 2
 
< 0.1%
-84.599961 2
 
< 0.1%
-94.149054 1
 
< 0.1%
-83.470749 1
 
< 0.1%
-97.405724 1
 
< 0.1%
-94.472943 1
 
< 0.1%
-122.679409 1
 
< 0.1%
-97.375174 1
 
< 0.1%
Other values (5267) 5267
99.7%
ValueCountFrequency (%)
-159.365025 1
< 0.1%
-158.075334 1
< 0.1%
-158.034591 1
< 0.1%
-158.005571 1
< 0.1%
-157.978362 1
< 0.1%
-157.974452 1
< 0.1%
-157.862004 1
< 0.1%
-157.843233 1
< 0.1%
-157.842705 1
< 0.1%
-156.454892 1
< 0.1%
ValueCountFrequency (%)
0 3
0.1%
-65.674303 1
 
< 0.1%
-65.807268 1
 
< 0.1%
-65.889641 1
 
< 0.1%
-65.995746 1
 
< 0.1%
-65.997031 1
 
< 0.1%
-66.015882 1
 
< 0.1%
-66.020899 1
 
< 0.1%
-66.076049 1
 
< 0.1%
-66.087612 1
 
< 0.1%

Y
Real number (ℝ)

Distinct5276
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.418339
Minimum0
Maximum64.856378
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:35.380143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.022818
Q132.943558
median36.165766
Q340.349658
95-th percentile44.537943
Maximum64.856378
Range64.856378
Interquartile range (IQR)7.4061002

Descriptive statistics

Standard deviation5.2857374
Coefficient of variation (CV)0.14513944
Kurtosis1.648455
Mean36.418339
Median Absolute Deviation (MAD)3.6488895
Skewness-0.22170713
Sum192361.67
Variance27.93902
MonotonicityNot monotonic
2023-10-22T12:06:35.598217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
0.1%
42.999723 2
 
< 0.1%
41.473938 2
 
< 0.1%
29.470675 2
 
< 0.1%
33.401188 2
 
< 0.1%
37.340296 1
 
< 0.1%
37.072913 1
 
< 0.1%
45.595564 1
 
< 0.1%
27.679204 1
 
< 0.1%
28.617165 1
 
< 0.1%
Other values (5266) 5266
99.7%
ValueCountFrequency (%)
0 3
0.1%
17.980621 1
 
< 0.1%
17.993681 1
 
< 0.1%
17.99736 1
 
< 0.1%
18.016813 1
 
< 0.1%
18.044336 1
 
< 0.1%
18.122626 1
 
< 0.1%
18.141237 1
 
< 0.1%
18.155224 1
 
< 0.1%
18.243301 1
 
< 0.1%
ValueCountFrequency (%)
64.856378 1
< 0.1%
61.568719 1
< 0.1%
61.309037 1
< 0.1%
61.211988 1
< 0.1%
61.192239 1
< 0.1%
61.140195 1
< 0.1%
60.564278 1
< 0.1%
57.811934 1
< 0.1%
55.375474 1
< 0.1%
48.818547 1
< 0.1%

objectid
Real number (ℝ)

UNIQUE 

Distinct5282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32408.828
Minimum29040
Maximum36706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:35.817152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum29040
5-th percentile29378.05
Q130475.25
median32133.5
Q334233.75
95-th percentile36273.95
Maximum36706
Range7666
Interquartile range (IQR)3758.5

Descriptive statistics

Standard deviation2175.711
Coefficient of variation (CV)0.067133282
Kurtosis-1.1003089
Mean32408.828
Median Absolute Deviation (MAD)1874.5
Skewness0.27077408
Sum1.7118343 × 108
Variance4733718.3
MonotonicityStrictly increasing
2023-10-22T12:06:36.049170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29040 1
 
< 0.1%
33551 1
 
< 0.1%
33543 1
 
< 0.1%
33542 1
 
< 0.1%
33541 1
 
< 0.1%
33540 1
 
< 0.1%
33536 1
 
< 0.1%
33535 1
 
< 0.1%
33533 1
 
< 0.1%
33532 1
 
< 0.1%
Other values (5272) 5272
99.8%
ValueCountFrequency (%)
29040 1
< 0.1%
29041 1
< 0.1%
29042 1
< 0.1%
29043 1
< 0.1%
29044 1
< 0.1%
29045 1
< 0.1%
29046 1
< 0.1%
29047 1
< 0.1%
29048 1
< 0.1%
29049 1
< 0.1%
ValueCountFrequency (%)
36706 1
< 0.1%
36705 1
< 0.1%
36704 1
< 0.1%
36702 1
< 0.1%
36700 1
< 0.1%
36698 1
< 0.1%
36695 1
< 0.1%
36693 1
< 0.1%
36690 1
< 0.1%
36688 1
< 0.1%
Distinct4824
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:36.478154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length42
Median length36
Mean length13.991102
Min length4

Characters and Unicode

Total characters73901
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4462 ?
Unique (%)84.5%

Sample

1st rowROGERS, AR
2nd rowTUNKHANNOCK, PA
3rd rowTAHLEQUAH OK
4th rowTRACY, CA
5th rowBENTONVILLE, AR
ValueCountFrequency (%)
tx 592
 
4.5%
fl 377
 
2.8%
ca 306
 
2.3%
ga 212
 
1.6%
nc 211
 
1.6%
il 180
 
1.4%
oh 167
 
1.3%
city 163
 
1.2%
la 158
 
1.2%
pa 157
 
1.2%
Other values (3263) 10775
81.0%
2023-10-22T12:06:37.208906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8025
 
10.9%
A 6403
 
8.7%
N 5146
 
7.0%
E 5135
 
6.9%
L 4890
 
6.6%
O 4633
 
6.3%
I 3823
 
5.2%
R 3744
 
5.1%
T 3683
 
5.0%
S 3416
 
4.6%
Other values (60) 25003
33.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60663
82.1%
Space Separator 8025
 
10.9%
Other Punctuation 2603
 
3.5%
Lowercase Letter 927
 
1.3%
Close Punctuation 683
 
0.9%
Open Punctuation 683
 
0.9%
Decimal Number 250
 
0.3%
Dash Punctuation 65
 
0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6403
 
10.6%
N 5146
 
8.5%
E 5135
 
8.5%
L 4890
 
8.1%
O 4633
 
7.6%
I 3823
 
6.3%
R 3744
 
6.2%
T 3683
 
6.1%
S 3416
 
5.6%
C 2580
 
4.3%
Other values (16) 17210
28.4%
Lowercase Letter
ValueCountFrequency (%)
a 114
12.3%
e 93
10.0%
o 92
9.9%
n 80
8.6%
l 78
8.4%
r 76
 
8.2%
i 57
 
6.1%
t 55
 
5.9%
s 55
 
5.9%
d 33
 
3.6%
Other values (14) 194
20.9%
Decimal Number
ValueCountFrequency (%)
1 83
33.2%
2 44
17.6%
0 38
15.2%
6 16
 
6.4%
3 15
 
6.0%
4 14
 
5.6%
7 14
 
5.6%
5 11
 
4.4%
8 10
 
4.0%
9 5
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 2430
93.4%
. 86
 
3.3%
/ 42
 
1.6%
& 38
 
1.5%
' 7
 
0.3%
Space Separator
ValueCountFrequency (%)
8025
100.0%
Close Punctuation
ValueCountFrequency (%)
) 683
100.0%
Open Punctuation
ValueCountFrequency (%)
( 683
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61590
83.3%
Common 12311
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6403
 
10.4%
N 5146
 
8.4%
E 5135
 
8.3%
L 4890
 
7.9%
O 4633
 
7.5%
I 3823
 
6.2%
R 3744
 
6.1%
T 3683
 
6.0%
S 3416
 
5.5%
C 2580
 
4.2%
Other values (40) 18137
29.4%
Common
ValueCountFrequency (%)
8025
65.2%
, 2430
 
19.7%
) 683
 
5.5%
( 683
 
5.5%
. 86
 
0.7%
1 83
 
0.7%
- 65
 
0.5%
2 44
 
0.4%
/ 42
 
0.3%
0 38
 
0.3%
Other values (10) 132
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8025
 
10.9%
A 6403
 
8.7%
N 5146
 
7.0%
E 5135
 
6.9%
L 4890
 
6.6%
O 4633
 
6.3%
I 3823
 
5.2%
R 3744
 
5.1%
T 3683
 
5.0%
S 3416
 
4.6%
Other values (60) 25003
33.8%

businessunit_number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3196.6717
Minimum1
Maximum11017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:37.446724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile279.05
Q11376.25
median2818.5
Q34946.75
95-th percentile6970.85
Maximum11017
Range11016
Interquartile range (IQR)3570.5

Descriptive statistics

Standard deviation2167.5044
Coefficient of variation (CV)0.67805035
Kurtosis-0.72529718
Mean3196.6717
Median Absolute Deviation (MAD)1698.5
Skewness0.47916053
Sum16884820
Variance4698075.3
MonotonicityNot monotonic
2023-10-22T12:06:37.669063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5907 1
 
< 0.1%
590 1
 
< 0.1%
59 1
 
< 0.1%
5899 1
 
< 0.1%
5898 1
 
< 0.1%
5894 1
 
< 0.1%
5893 1
 
< 0.1%
5891 1
 
< 0.1%
5890 1
 
< 0.1%
Other values (5272) 5272
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
11017 1
< 0.1%
9894 1
< 0.1%
8958 1
< 0.1%
8930 1
< 0.1%
8861 1
< 0.1%
8331 1
< 0.1%
8299 1
< 0.1%
8298 1
< 0.1%
8297 1
< 0.1%
8296 1
< 0.1%

businessunit_banner_description
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
WM Supercenter
3591 
Neighborhood Market
687 
Sam's Club
605 
Wal-Mart
364 
WM On Campus/RX Facilities
 
22
Other values (5)
 
13

Length

Max length26
Median length14
Mean length13.839644
Min length5

Characters and Unicode

Total characters73101
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowWM Supercenter
2nd rowWM Supercenter
3rd rowWM Supercenter
4th rowWM Supercenter
5th rowWM Supercenter

Common Values

ValueCountFrequency (%)
WM Supercenter 3591
68.0%
Neighborhood Market 687
 
13.0%
Sam's Club 605
 
11.5%
Wal-Mart 364
 
6.9%
WM On Campus/RX Facilities 22
 
0.4%
Walmart Fuel Station 8
 
0.2%
STAND ALONE PICKUP 2
 
< 0.1%
MOBILE OPTICAL 1
 
< 0.1%
WM ONLINE PICKUP/DELIVERY 1
 
< 0.1%
Other 1
 
< 0.1%

Length

2023-10-22T12:06:37.925245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:38.130320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
wm 3614
35.2%
supercenter 3591
35.0%
neighborhood 687
 
6.7%
market 687
 
6.7%
sam's 605
 
5.9%
club 605
 
5.9%
wal-mart 364
 
3.5%
on 22
 
0.2%
campus/rx 22
 
0.2%
facilities 22
 
0.2%
Other values (11) 35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 12178
16.7%
r 8929
12.2%
4972
 
6.8%
t 4689
 
6.4%
M 4666
 
6.4%
u 4226
 
5.8%
S 4206
 
5.8%
W 3986
 
5.5%
n 3621
 
5.0%
p 3613
 
4.9%
Other values (33) 18015
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52806
72.2%
Uppercase Letter 14331
 
19.6%
Space Separator 4972
 
6.8%
Other Punctuation 628
 
0.9%
Dash Punctuation 364
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 4666
32.6%
S 4206
29.3%
W 3986
27.8%
N 693
 
4.8%
C 631
 
4.4%
F 30
 
0.2%
O 28
 
0.2%
R 23
 
0.2%
X 22
 
0.2%
I 7
 
< 0.1%
Other values (11) 39
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 12178
23.1%
r 8929
16.9%
t 4689
 
8.9%
u 4226
 
8.0%
n 3621
 
6.9%
p 3613
 
6.8%
c 3613
 
6.8%
a 2088
 
4.0%
o 2069
 
3.9%
h 1375
 
2.6%
Other values (8) 6405
12.1%
Other Punctuation
ValueCountFrequency (%)
' 605
96.3%
/ 23
 
3.7%
Space Separator
ValueCountFrequency (%)
4972
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67137
91.8%
Common 5964
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12178
18.1%
r 8929
13.3%
t 4689
 
7.0%
M 4666
 
6.9%
u 4226
 
6.3%
S 4206
 
6.3%
W 3986
 
5.9%
n 3621
 
5.4%
p 3613
 
5.4%
c 3613
 
5.4%
Other values (29) 13410
20.0%
Common
ValueCountFrequency (%)
4972
83.4%
' 605
 
10.1%
- 364
 
6.1%
/ 23
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12178
16.7%
r 8929
12.2%
4972
 
6.8%
t 4689
 
6.4%
M 4666
 
6.4%
u 4226
 
5.8%
S 4206
 
5.8%
W 3986
 
5.5%
n 3621
 
5.0%
p 3613
 
4.9%
Other values (33) 18015
24.6%

businessunit_type_description
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
Retail
4677 
Wholesale
605 

Length

Max length9
Median length6
Mean length6.3436198
Min length6

Characters and Unicode

Total characters33507
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRetail
2nd rowRetail
3rd rowRetail
4th rowRetail
5th rowRetail

Common Values

ValueCountFrequency (%)
Retail 4677
88.5%
Wholesale 605
 
11.5%

Length

2023-10-22T12:06:38.377057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:38.549695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
retail 4677
88.5%
wholesale 605
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 5887
17.6%
l 5887
17.6%
a 5282
15.8%
R 4677
14.0%
t 4677
14.0%
i 4677
14.0%
W 605
 
1.8%
h 605
 
1.8%
o 605
 
1.8%
s 605
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28225
84.2%
Uppercase Letter 5282
 
15.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5887
20.9%
l 5887
20.9%
a 5282
18.7%
t 4677
16.6%
i 4677
16.6%
h 605
 
2.1%
o 605
 
2.1%
s 605
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
R 4677
88.5%
W 605
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 33507
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5887
17.6%
l 5887
17.6%
a 5282
15.8%
R 4677
14.0%
t 4677
14.0%
i 4677
14.0%
W 605
 
1.8%
h 605
 
1.8%
o 605
 
1.8%
s 605
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5887
17.6%
l 5887
17.6%
a 5282
15.8%
R 4677
14.0%
t 4677
14.0%
i 4677
14.0%
W 605
 
1.8%
h 605
 
1.8%
o 605
 
1.8%
s 605
 
1.8%

businessunit_isstoreopen
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
True
5261 
False
 
21
ValueCountFrequency (%)
True 5261
99.6%
False 21
 
0.4%
2023-10-22T12:06:38.681355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct5266
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:39.033350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length41
Median length35
Mean length17.632526
Min length3

Characters and Unicode

Total characters93135
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5257 ?
Unique (%)99.5%

Sample

1st row2110 W WALNUT ST
2nd row808 HUNTER HWY
3rd row2020 S MUSKOGEE AVE
4th row3010 W GRANT LINE RD
5th row406 S WALTON BLVD
ValueCountFrequency (%)
rd 998
 
5.0%
st 791
 
4.0%
ave 619
 
3.1%
dr 572
 
2.9%
blvd 570
 
2.9%
s 534
 
2.7%
w 506
 
2.6%
n 502
 
2.5%
e 469
 
2.4%
highway 357
 
1.8%
Other values (5706) 13867
70.1%
2023-10-22T12:06:39.692121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14503
 
15.6%
E 5579
 
6.0%
R 4997
 
5.4%
A 4895
 
5.3%
0 4550
 
4.9%
1 4186
 
4.5%
S 3819
 
4.1%
N 3756
 
4.0%
T 3702
 
4.0%
D 3464
 
3.7%
Other values (42) 39684
42.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56288
60.4%
Decimal Number 22207
 
23.8%
Space Separator 14503
 
15.6%
Other Punctuation 103
 
0.1%
Dash Punctuation 24
 
< 0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5579
 
9.9%
R 4997
 
8.9%
A 4895
 
8.7%
S 3819
 
6.8%
N 3756
 
6.7%
T 3702
 
6.6%
D 3464
 
6.2%
L 3372
 
6.0%
O 2969
 
5.3%
I 2735
 
4.9%
Other values (17) 17000
30.2%
Decimal Number
ValueCountFrequency (%)
0 4550
20.5%
1 4186
18.8%
5 2654
12.0%
2 2543
11.5%
3 1923
8.7%
4 1600
 
7.2%
7 1285
 
5.8%
6 1243
 
5.6%
9 1118
 
5.0%
8 1105
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
y 2
20.0%
d 2
20.0%
k 1
10.0%
w 1
10.0%
c 1
10.0%
a 1
10.0%
m 1
10.0%
e 1
10.0%
Other Punctuation
ValueCountFrequency (%)
. 88
85.4%
# 6
 
5.8%
, 4
 
3.9%
/ 3
 
2.9%
' 2
 
1.9%
Space Separator
ValueCountFrequency (%)
14503
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56298
60.4%
Common 36837
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5579
 
9.9%
R 4997
 
8.9%
A 4895
 
8.7%
S 3819
 
6.8%
N 3756
 
6.7%
T 3702
 
6.6%
D 3464
 
6.2%
L 3372
 
6.0%
O 2969
 
5.3%
I 2735
 
4.9%
Other values (25) 17010
30.2%
Common
ValueCountFrequency (%)
14503
39.4%
0 4550
 
12.4%
1 4186
 
11.4%
5 2654
 
7.2%
2 2543
 
6.9%
3 1923
 
5.2%
4 1600
 
4.3%
7 1285
 
3.5%
6 1243
 
3.4%
9 1118
 
3.0%
Other values (7) 1232
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93134
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14503
 
15.6%
E 5579
 
6.0%
R 4997
 
5.4%
A 4895
 
5.3%
0 4550
 
4.9%
1 4186
 
4.5%
S 3819
 
4.1%
N 3756
 
4.0%
T 3702
 
4.0%
D 3464
 
3.7%
Other values (41) 39683
42.6%
None
ValueCountFrequency (%)
Ñ 1
100.0%
Distinct2686
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:40.023762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.8080273
Min length3

Characters and Unicode

Total characters46524
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1779 ?
Unique (%)33.7%

Sample

1st rowROGERS
2nd rowTUNKHANNOCK
3rd rowTAHLEQUAH
4th rowTRACY
5th rowBENTONVILLE
ValueCountFrequency (%)
city 156
 
2.4%
san 68
 
1.0%
beach 61
 
0.9%
springs 59
 
0.9%
fort 53
 
0.8%
lake 45
 
0.7%
north 44
 
0.7%
west 42
 
0.6%
park 38
 
0.6%
las 37
 
0.6%
Other values (2541) 6025
90.9%
2023-10-22T12:06:40.740250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4466
 
9.6%
E 4450
 
9.6%
O 3788
 
8.1%
N 3714
 
8.0%
L 3700
 
8.0%
R 3231
 
6.9%
I 2948
 
6.3%
S 2676
 
5.8%
T 2551
 
5.5%
C 1574
 
3.4%
Other values (19) 13426
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 45176
97.1%
Space Separator 1346
 
2.9%
Other Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4466
 
9.9%
E 4450
 
9.9%
O 3788
 
8.4%
N 3714
 
8.2%
L 3700
 
8.2%
R 3231
 
7.2%
I 2948
 
6.5%
S 2676
 
5.9%
T 2551
 
5.6%
C 1574
 
3.5%
Other values (16) 12078
26.7%
Space Separator
ValueCountFrequency (%)
1346
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45176
97.1%
Common 1348
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4466
 
9.9%
E 4450
 
9.9%
O 3788
 
8.4%
N 3714
 
8.2%
L 3700
 
8.2%
R 3231
 
7.2%
I 2948
 
6.5%
S 2676
 
5.9%
T 2551
 
5.6%
C 1574
 
3.5%
Other values (16) 12078
26.7%
Common
ValueCountFrequency (%)
1346
99.9%
. 1
 
0.1%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4466
 
9.6%
E 4450
 
9.6%
O 3788
 
8.1%
N 3714
 
8.0%
L 3700
 
8.0%
R 3231
 
6.9%
I 2948
 
6.3%
S 2676
 
5.8%
T 2551
 
5.5%
C 1574
 
3.4%
Other values (19) 13426
28.9%
Distinct1236
Distinct (%)23.4%
Missing1
Missing (%)< 0.1%
Memory size41.4 KiB
2023-10-22T12:06:41.203396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length28
Median length25
Mean length7.3130089
Min length3

Characters and Unicode

Total characters38620
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)10.9%

Sample

1st rowBENTON
2nd rowWYOMING
3rd rowCHEROKEE
4th rowSAN JOAQUIN
5th rowBENTON
ValueCountFrequency (%)
st 82
 
1.4%
jefferson 74
 
1.3%
maricopa 71
 
1.2%
orange 69
 
1.2%
san 67
 
1.1%
harris 61
 
1.0%
washington 58
 
1.0%
dallas 56
 
1.0%
tarrant 53
 
0.9%
city 53
 
0.9%
Other values (1251) 5224
89.0%
2023-10-22T12:06:42.216382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4442
11.5%
E 3651
 
9.5%
O 3219
 
8.3%
N 3171
 
8.2%
R 2868
 
7.4%
L 2636
 
6.8%
S 2297
 
5.9%
I 2105
 
5.5%
T 1745
 
4.5%
C 1456
 
3.8%
Other values (31) 11030
28.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37906
98.2%
Space Separator 588
 
1.5%
Other Punctuation 86
 
0.2%
Dash Punctuation 23
 
0.1%
Lowercase Letter 17
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4442
11.7%
E 3651
 
9.6%
O 3219
 
8.5%
N 3171
 
8.4%
R 2868
 
7.6%
L 2636
 
7.0%
S 2297
 
6.1%
I 2105
 
5.6%
T 1745
 
4.6%
C 1456
 
3.8%
Other values (16) 10316
27.2%
Lowercase Letter
ValueCountFrequency (%)
r 2
11.8%
n 2
11.8%
e 2
11.8%
w 2
11.8%
a 2
11.8%
l 2
11.8%
i 1
5.9%
t 1
5.9%
c 1
5.9%
s 1
5.9%
Other Punctuation
ValueCountFrequency (%)
. 82
95.3%
' 4
 
4.7%
Space Separator
ValueCountFrequency (%)
588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37923
98.2%
Common 697
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4442
11.7%
E 3651
 
9.6%
O 3219
 
8.5%
N 3171
 
8.4%
R 2868
 
7.6%
L 2636
 
7.0%
S 2297
 
6.1%
I 2105
 
5.6%
T 1745
 
4.6%
C 1456
 
3.8%
Other values (27) 10333
27.2%
Common
ValueCountFrequency (%)
588
84.4%
. 82
 
11.8%
- 23
 
3.3%
' 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4442
11.5%
E 3651
 
9.5%
O 3219
 
8.3%
N 3171
 
8.2%
R 2868
 
7.4%
L 2636
 
6.8%
S 2297
 
5.9%
I 2105
 
5.5%
T 1745
 
4.5%
C 1456
 
3.8%
Other values (31) 11030
28.6%
Distinct52
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:42.467529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10564
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAR
2nd rowPA
3rd rowOK
4th rowCA
5th rowAR
ValueCountFrequency (%)
tx 604
 
11.4%
fl 396
 
7.5%
ca 310
 
5.9%
nc 215
 
4.1%
ga 214
 
4.1%
il 185
 
3.5%
oh 172
 
3.3%
pa 159
 
3.0%
mo 156
 
3.0%
tn 150
 
2.8%
Other values (42) 2721
51.5%
2023-10-22T12:06:42.870201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1566
14.8%
N 948
 
9.0%
T 870
 
8.2%
L 864
 
8.2%
C 789
 
7.5%
I 645
 
6.1%
M 640
 
6.1%
O 612
 
5.8%
X 604
 
5.7%
F 396
 
3.7%
Other values (14) 2630
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10564
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1566
14.8%
N 948
 
9.0%
T 870
 
8.2%
L 864
 
8.2%
C 789
 
7.5%
I 645
 
6.1%
M 640
 
6.1%
O 612
 
5.8%
X 604
 
5.7%
F 396
 
3.7%
Other values (14) 2630
24.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 10564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1566
14.8%
N 948
 
9.0%
T 870
 
8.2%
L 864
 
8.2%
C 789
 
7.5%
I 645
 
6.1%
M 640
 
6.1%
O 612
 
5.8%
X 604
 
5.7%
F 396
 
3.7%
Other values (14) 2630
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1566
14.8%
N 948
 
9.0%
T 870
 
8.2%
L 864
 
8.2%
C 789
 
7.5%
I 645
 
6.1%
M 640
 
6.1%
O 612
 
5.8%
X 604
 
5.7%
F 396
 
3.7%
Other values (14) 2630
24.9%
Distinct5186
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:43.186263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9933737
Min length5

Characters and Unicode

Total characters52785
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5090 ?
Unique (%)96.4%

Sample

1st row72756-3246
2nd row18657-8071
3rd row74464-5439
4th row95304-9402
5th row72712-5705
ValueCountFrequency (%)
63303-3526 2
 
< 0.1%
72712-0000 2
 
< 0.1%
76548-0000 2
 
< 0.1%
20724-1810 2
 
< 0.1%
85308-0501 2
 
< 0.1%
78229-5313 2
 
< 0.1%
07728-2524 2
 
< 0.1%
76549-0000 2
 
< 0.1%
12901-6508 2
 
< 0.1%
32547-3002 2
 
< 0.1%
Other values (5176) 5262
99.6%
2023-10-22T12:06:43.815431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7018
13.3%
1 5389
10.2%
3 5367
10.2%
2 5315
10.1%
- 5275
10.0%
4 4649
8.8%
5 4443
8.4%
7 4428
8.4%
6 4122
7.8%
8 3624
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47510
90.0%
Dash Punctuation 5275
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7018
14.8%
1 5389
11.3%
3 5367
11.3%
2 5315
11.2%
4 4649
9.8%
5 4443
9.4%
7 4428
9.3%
6 4122
8.7%
8 3624
7.6%
9 3155
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 5275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52785
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7018
13.3%
1 5389
10.2%
3 5367
10.2%
2 5315
10.1%
- 5275
10.0%
4 4649
8.8%
5 4443
8.4%
7 4428
8.4%
6 4122
7.8%
8 3624
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7018
13.3%
1 5389
10.2%
3 5367
10.2%
2 5315
10.1%
- 5275
10.0%
4 4649
8.8%
5 4443
8.4%
7 4428
8.4%
6 4122
7.8%
8 3624
6.9%

businessunit_status_code
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4670579
Minimum0
Maximum6
Zeros27
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:44.003324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q35
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5229587
Coefficient of variation (CV)0.43926543
Kurtosis-1.8932061
Mean3.4670579
Median Absolute Deviation (MAD)0
Skewness0.0029059456
Sum18313
Variance2.3194032
MonotonicityNot monotonic
2023-10-22T12:06:44.158891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 2655
50.3%
5 2572
48.7%
0 27
 
0.5%
6 22
 
0.4%
1 4
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 27
 
0.5%
1 4
 
0.1%
2 2655
50.3%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 2572
48.7%
6 22
 
0.4%
ValueCountFrequency (%)
6 22
 
0.4%
5 2572
48.7%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 2655
50.3%
1 4
 
0.1%
0 27
 
0.5%

op_status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.4 KiB
Open
5279 
Closed
 
3

Length

Max length6
Median length4
Mean length4.0011359
Min length4

Characters and Unicode

Total characters21134
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowOpen
3rd rowOpen
4th rowOpen
5th rowOpen

Common Values

ValueCountFrequency (%)
Open 5279
99.9%
Closed 3
 
0.1%

Length

2023-10-22T12:06:44.350257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:44.508139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
open 5279
99.9%
closed 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 5282
25.0%
O 5279
25.0%
p 5279
25.0%
n 5279
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15852
75.0%
Uppercase Letter 5282
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5282
33.3%
p 5279
33.3%
n 5279
33.3%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
O 5279
99.9%
C 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21134
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5282
25.0%
O 5279
25.0%
p 5279
25.0%
n 5279
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5282
25.0%
O 5279
25.0%
p 5279
25.0%
n 5279
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Modified_Operating_Hours_
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing5279
Missing (%)99.9%
Memory size10.4 KiB
False
 
3
(Missing)
5279 
ValueCountFrequency (%)
False 3
 
0.1%
(Missing) 5279
99.9%
2023-10-22T12:06:44.680114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Pharmacy_open_to_public_
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing32
Missing (%)0.6%
Memory size10.4 KiB
True
5249 
False
 
1
(Missing)
 
32
ValueCountFrequency (%)
True 5249
99.4%
False 1
 
< 0.1%
(Missing) 32
 
0.6%
2023-10-22T12:06:44.803061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing32
Missing (%)0.6%
Memory size10.4 KiB
True
5250 
(Missing)
 
32
ValueCountFrequency (%)
True 5250
99.4%
(Missing) 32
 
0.6%
2023-10-22T12:06:44.973725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

bu_num
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3196.6717
Minimum1
Maximum11017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.4 KiB
2023-10-22T12:06:45.167684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile279.05
Q11376.25
median2818.5
Q34946.75
95-th percentile6970.85
Maximum11017
Range11016
Interquartile range (IQR)3570.5

Descriptive statistics

Standard deviation2167.5044
Coefficient of variation (CV)0.67805035
Kurtosis-0.72529718
Mean3196.6717
Median Absolute Deviation (MAD)1698.5
Skewness0.47916053
Sum16884820
Variance4698075.3
MonotonicityNot monotonic
2023-10-22T12:06:45.426181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5907 1
 
< 0.1%
590 1
 
< 0.1%
59 1
 
< 0.1%
5899 1
 
< 0.1%
5898 1
 
< 0.1%
5894 1
 
< 0.1%
5893 1
 
< 0.1%
5891 1
 
< 0.1%
5890 1
 
< 0.1%
Other values (5272) 5272
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
11017 1
< 0.1%
9894 1
< 0.1%
8958 1
< 0.1%
8930 1
< 0.1%
8861 1
< 0.1%
8331 1
< 0.1%
8299 1
< 0.1%
8298 1
< 0.1%
8297 1
< 0.1%
8296 1
< 0.1%

Grocery_delivery_service
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing467
Missing (%)8.8%
Memory size10.4 KiB
False
3157 
True
1658 
(Missing)
467 
ValueCountFrequency (%)
False 3157
59.8%
True 1658
31.4%
(Missing) 467
 
8.8%
2023-10-22T12:06:45.582685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Grocery_delivery_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing2417
Missing (%)45.8%
Memory size41.4 KiB
Available
2346 
Not Available
519 

Length

Max length13
Median length9
Mean length9.7246073
Min length9

Characters and Unicode

Total characters27861
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAvailable
2nd rowNot Available
3rd rowNot Available
4th rowAvailable
5th rowAvailable

Common Values

ValueCountFrequency (%)
Available 2346
44.4%
Not Available 519
 
9.8%
(Missing) 2417
45.8%

Length

2023-10-22T12:06:45.748447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:45.916308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
available 2865
84.7%
not 519
 
15.3%

Most occurring characters

ValueCountFrequency (%)
a 5730
20.6%
l 5730
20.6%
A 2865
10.3%
v 2865
10.3%
i 2865
10.3%
b 2865
10.3%
e 2865
10.3%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23958
86.0%
Uppercase Letter 3384
 
12.1%
Space Separator 519
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5730
23.9%
l 5730
23.9%
v 2865
12.0%
i 2865
12.0%
b 2865
12.0%
e 2865
12.0%
o 519
 
2.2%
t 519
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 2865
84.7%
N 519
 
15.3%
Space Separator
ValueCountFrequency (%)
519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27342
98.1%
Common 519
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5730
21.0%
l 5730
21.0%
A 2865
10.5%
v 2865
10.5%
i 2865
10.5%
b 2865
10.5%
e 2865
10.5%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%
Common
ValueCountFrequency (%)
519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5730
20.6%
l 5730
20.6%
A 2865
10.3%
v 2865
10.3%
i 2865
10.3%
b 2865
10.3%
e 2865
10.3%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%

Online_grocery_pickup
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing138
Missing (%)2.6%
Memory size10.4 KiB
True
3616 
False
1528 
(Missing)
 
138
ValueCountFrequency (%)
True 3616
68.5%
False 1528
28.9%
(Missing) 138
 
2.6%
2023-10-22T12:06:46.078901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Online_grocery_pickup_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing1505
Missing (%)28.5%
Memory size41.4 KiB
Available
3144 
Not Available
633 

Length

Max length13
Median length9
Mean length9.6703733
Min length9

Characters and Unicode

Total characters36525
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Available
2nd rowAvailable
3rd rowAvailable
4th rowNot Available
5th rowNot Available

Common Values

ValueCountFrequency (%)
Available 3144
59.5%
Not Available 633
 
12.0%
(Missing) 1505
28.5%

Length

2023-10-22T12:06:46.261318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:46.427330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
available 3777
85.6%
not 633
 
14.4%

Most occurring characters

ValueCountFrequency (%)
a 7554
20.7%
l 7554
20.7%
A 3777
10.3%
v 3777
10.3%
i 3777
10.3%
b 3777
10.3%
e 3777
10.3%
N 633
 
1.7%
o 633
 
1.7%
t 633
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31482
86.2%
Uppercase Letter 4410
 
12.1%
Space Separator 633
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7554
24.0%
l 7554
24.0%
v 3777
12.0%
i 3777
12.0%
b 3777
12.0%
e 3777
12.0%
o 633
 
2.0%
t 633
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 3777
85.6%
N 633
 
14.4%
Space Separator
ValueCountFrequency (%)
633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35892
98.3%
Common 633
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7554
21.0%
l 7554
21.0%
A 3777
10.5%
v 3777
10.5%
i 3777
10.5%
b 3777
10.5%
e 3777
10.5%
N 633
 
1.8%
o 633
 
1.8%
t 633
 
1.8%
Common
ValueCountFrequency (%)
633
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7554
20.7%
l 7554
20.7%
A 3777
10.3%
v 3777
10.3%
i 3777
10.3%
b 3777
10.3%
e 3777
10.3%
N 633
 
1.7%
o 633
 
1.7%
t 633
 
1.7%

Interactions

2023-10-22T12:06:32.674296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:27.486337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:28.680474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.698167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.781591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.772278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.837135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:27.712667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:28.846062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.889992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.949060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.936347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.982374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:27.869762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.001302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.053758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.099987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.087315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:33.138146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:28.039556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.186561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.227227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.288895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.246084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:33.275978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:28.339013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.373908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.404682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.446735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.387789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:33.427838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:28.516366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:29.545550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:30.604137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:31.618335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:32.531674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-22T12:06:46.552104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
XYobjectidbusinessunit_numberbusinessunit_status_codebu_numbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenop_statusPharmacy_open_to_public_Grocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
X1.0000.084-0.0500.0250.0100.0250.2620.0210.0360.0000.0000.0450.0570.0290.051
Y0.0841.000-0.1810.0070.1000.0070.2220.0550.0460.0000.0000.0590.0000.0000.010
objectid-0.050-0.1811.0000.476-0.2010.4760.1960.4980.0240.0410.0000.0590.0000.0370.000
businessunit_number0.0250.0070.4761.000-0.5511.0000.3010.6640.0400.0000.0000.1160.0030.0530.037
businessunit_status_code0.0100.100-0.201-0.5511.000-0.5510.2830.0620.9760.0000.0000.0430.0000.0760.000
bu_num0.0250.0070.4761.000-0.5511.0000.3010.6640.0400.0000.0000.1160.0030.0530.037
businessunit_banner_description0.2620.2220.1960.3010.2830.3011.0000.9990.2170.0000.0000.2000.0480.1720.044
businessunit_type_description0.0210.0550.4980.6640.0620.6640.9991.0000.0000.0000.0000.1870.0000.1050.009
businessunit_isstoreopen0.0360.0460.0240.0400.9760.0400.2170.0001.0000.0000.0000.0000.0000.0110.000
op_status0.0000.0000.0410.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Pharmacy_open_to_public_0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.000
Grocery_delivery_service0.0450.0590.0590.1160.0430.1160.2000.1870.0000.0000.0001.0000.3310.4220.249
Grocery_delivery_status0.0570.0000.0000.0030.0000.0030.0480.0000.0000.0001.0000.3311.0000.2360.918
Online_grocery_pickup0.0290.0000.0370.0530.0760.0530.1720.1050.0110.0000.0000.4220.2361.0000.175
Online_grocery_pickup_status0.0510.0100.0000.0370.0000.0370.0440.0090.0000.0001.0000.2490.9180.1751.000

Missing values

2023-10-22T12:06:33.672253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T12:06:34.158895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T12:06:34.605386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

XYobjectidbusinessunit_namebusinessunit_numberbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenfacilitydetails_location_locatifacilitydetails_location_loca_1facilitydetails_location_loca_2facilitydetails_location_loca_3facilitydetails_location_loca_8businessunit_status_codeop_statusModified_Operating_Hours_Pharmacy_open_to_public_Fuel_Station_open_to_public_bu_numGrocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
0-94.14905436.33130029040ROGERS, AR1WM SupercenterRetailTrue2110 W WALNUT STROGERSBENTONAR72756-32465OpenNaNYY1YesNaNYesNot Available
1-75.94744141.52398129041TUNKHANNOCK, PA2024WM SupercenterRetailTrue808 HUNTER HWYTUNKHANNOCKWYOMINGPA18657-80715OpenNaNYY2024NoAvailableNoAvailable
2-94.97981435.88880729042TAHLEQUAH OK10WM SupercenterRetailTrue2020 S MUSKOGEE AVETAHLEQUAHCHEROKEEOK74464-54395OpenNaNYY10NoNaNNoAvailable
3-121.47171537.75165929043TRACY, CA2025WM SupercenterRetailTrue3010 W GRANT LINE RDTRACYSAN JOAQUINCA95304-94025OpenNaNYY2025NoNot AvailableNoNot Available
4-94.22484436.36817329044BENTONVILLE, AR100WM SupercenterRetailTrue406 S WALTON BLVDBENTONVILLEBENTONAR72712-57055OpenNaNYY100NoNot AvailableNoNot Available
5-66.64257817.99736029045PONCE-PR2026WM SupercenterRetailTrue3305 AVE.BARAMAYA SUITE 100PONCEPONCEPR00728-00005OpenNaNYY2026NaNNaNYesNaN
6-97.47700325.92514229046BROWNSVILLE TX1000WM SupercenterRetailTrue2721 BOCA CHICA BLVDBROWNSVILLECAMERONTX78521-35015OpenNaNYY1000YesNaNYesAvailable
7-78.83505239.62490329047LAVALE, MD2027WM SupercenterRetailTrue12500 COUNTRY CLUB MALL RDLAVALEALLEGANYMD21502-75535OpenNaNYY2027YesAvailableYesAvailable
8-104.66489838.23231629048PUEBLO, CO1001WM SupercenterRetailTrue4080 W NORTHERN AVEPUEBLOPUEBLOCO81005-35035OpenNaNYY1001YesAvailableYesAvailable
9-117.45533933.93707129049RIVERSIDE (S), CA2028WM SupercenterRetailTrue5200 VAN BUREN BLVDRIVERSIDERIVERSIDECA92503-25445OpenNaNYY2028NoNaNYesNaN
XYobjectidbusinessunit_namebusinessunit_numberbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenfacilitydetails_location_locatifacilitydetails_location_loca_1facilitydetails_location_loca_2facilitydetails_location_loca_3facilitydetails_location_loca_8businessunit_status_codeop_statusModified_Operating_Hours_Pharmacy_open_to_public_Fuel_Station_open_to_public_bu_numGrocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
5272-82.45571829.06330936688DUNNELLON FL960WM SupercenterRetailTrue11012 NO. WILLIAMS STDUNNELLONMARIONFL34432-83105OpenNaNYY960YesAvailableYesAvailable
5273-90.49828530.07471636690LAPLACE LA961WM SupercenterRetailTrue1616 W AIRLINE HWYLA PLACEST. JOHN THE BAPTIST PARISHLA70068-33315OpenNaNYY961NoAvailableYesAvailable
5274-104.52327537.13872836693TRINIDAD CO962WM SupercenterRetailTrue2921 TOUPAL DRTRINIDADLAS ANIMASCO81082-87405OpenNaNYY962YesAvailableYesAvailable
5275-97.79486232.73149336695WEATHERFORD TX963WM SupercenterRetailTrue1836 S MAIN STWEATHERFORDPARKERTX76086-55065OpenNaNYY963NoAvailableYesAvailable
5276-106.31159631.68289836698EL PASO (S) TX964WM SupercenterRetailTrue9441 ALAMEDA AVEEL PASOEL PASOTX79907-56015OpenNaNYY964YesAvailableYesAvailable
5277-90.50857744.02102436700TOMAH WI965WM SupercenterRetailTrue222 W MCCOY BLVDTOMAHMONROEWI54660-32915OpenNaNYY965NoNaNYesNaN
5278-108.56199437.34656836702CORTEZ CO966WM SupercenterRetailTrue1835 E MAIN STCORTEZMONTEZUMACO81321-30375OpenNaNYY966YesNaNYesNaN
5279-82.63121828.45839736704SPRINGHILL / BROOKSVILLE967WM SupercenterRetailTrue1485 COMMERCIAL WAYSPRING HILLHERNANDOFL34606-45255OpenNaNYY967YesAvailableYesAvailable
5280-81.72225128.00665136705WINTER HAVEN FL968WM SupercenterRetailTrue355 CYPRESS GARDENS BLVDWINTER HAVENPOLKFL33880-44525OpenNaNYY968YesAvailableYesAvailable
5281-89.08869730.42322436706GULFPORT MS969WM SupercenterRetailTrue9350 HIGHWAY 49GULFPORTHARRISONMS39503-42135OpenNaNYY969NoNot AvailableYesAvailable